Demystifying `torch.Tensor.refine_names()`: Function or Typo?

1. Custom Function
   It's possible that torch.Tensor.refine_names() is a custom function created for a specific project or library. These functions are not part of the core PyTorch library and their functionality depends on the code that implements them.

2. Typo
   There might be a typo in the function name. PyTorch has a function named torch.Tensor.refine_names_(inplace=True) which is used for in-place refining of tensor dimension names. This function modifies the tensor's names directly (indicated by the trailing underscore) to make them more human-readable.

• It shortens excessively long names.
• It replaces invalid characters or underscores with more readable separators (like hyphens).
• It iterates over the tensor's dimension names.

If you're unsure about the exact function you're dealing with, it's best to consult the documentation for the specific library or project where you encountered it.

In the absence of official documentation, here are some resources that might help you understand similar functionalities in PyTorch:

• Working with Tensor Names: [PyTorch tensor names ON pytorch.org]
• PyTorch Tensor Documentation: [PyTorch tensors ON pytorch.org]

---

import torch

# Create a tensor with default dimension names
tensor = torch.randn(3, 4, 5)
print(tensor.names())  # Output: ('', '', '') - Anonymous names

# Refine the names in-place
tensor.refine_names_(inplace=True)
print(tensor.names())  # Output: ('dim0', 'dim1', 'dim2') - More readable names

# Access dimensions by name
print(tensor.shape[0])  # Accesses the first dimension using the refined name

---

1. • You can iterate through the tensor's dimension names and use string manipulation techniques (like regular expressions or string replacement) to achieve your desired level of refinement. This approach offers full control but might be less concise.

2. Custom Function
   

   • If you have specific naming conventions, you can create your own function to refine names based on your needs. This allows for customization but requires additional code maintenance.

3. torch.Tensor.rename(names)
   

   • PyTorch offers the rename function to explicitly set dimension names for a tensor:

   tensor = torch.randn(3, 4, 5)
   new_names = ('batch', 'channels', 'features')
   tensor = tensor.rename(names=new_names)
   

   This gives you complete control over the naming scheme but doesn't automatically refine existing names.

4. Descriptive Variable Names
   

   • While not a direct replacement, using descriptive variable names when creating tensors can improve readability from the start. This can often eliminate the need for extensive name refinement later.